# Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception

arXiv: https://arxiv.org/abs/1905.00160 · HTML: https://mlsanity.com/paper/1905.00160/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2019.

## Built on by (most cited first, 29 of 29)

- [Infinigen Indoors: Photorealistic Indoor Scenes using Procedural Generation](https://mlsanity.com/paper/2406.11824/) (2024)
- [Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation](https://mlsanity.com/paper/2107.05399/) (2021)
- [A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook](https://mlsanity.com/paper/2401.01454/) (2024)
- [Synthetic Datasets for Autonomous Driving: A Survey](https://mlsanity.com/paper/2304.12205/) (2023)
- [GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation](https://mlsanity.com/paper/2207.09763/) (2022)
- [Deformation and Correspondence Aware Unsupervised Synthetic-to-Real Scene Flow Estimation for Point Clouds](https://mlsanity.com/paper/2203.16895/) (2022)
- [CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection](https://mlsanity.com/paper/2212.00244/) (2022)
- [Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping](https://mlsanity.com/paper/2111.11348/) (2021)
- [Leveraging Synthetic Data in Object Detection on Unmanned Aerial Vehicles](https://mlsanity.com/paper/2112.12252/) (2021)
- [PCGen: Point Cloud Generator for LiDAR Simulation](https://mlsanity.com/paper/2210.08738/) (2022)
- [Learning from Active Human Involvement through Proxy Value Propagation](https://mlsanity.com/paper/2502.03369/) (2025)
- [The Second Monocular Depth Estimation Challenge](https://mlsanity.com/paper/2304.07051/) (2023)
- [Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation](https://mlsanity.com/paper/2310.20293/) (2023)
- [High-Resolution Synthetic RGB-D Datasets for Monocular Depth Estimation](https://mlsanity.com/paper/2305.01732/) (2023)
- [Mind The Edge: Refining Depth Edges in Sparsely-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2212.05315/) (2022)
- [A Realism Metric for Generated LiDAR Point Clouds](https://mlsanity.com/paper/2208.14958/) (2022)
- [Syn4D: A Multiview Synthetic 4D Dataset](https://mlsanity.com/paper/2605.05207/) (2026)
- [PLT-D3: A High-fidelity Dynamic Driving Simulation Dataset for Stereo Depth and Scene Flow](https://mlsanity.com/paper/2406.07667/) (2024)
- [RealDriveSim: A Realistic Multi-Modal Multi-Task Synthetic Dataset for Autonomous Driving](https://mlsanity.com/paper/2506.16319/) (2025)
- [SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspection](https://mlsanity.com/paper/2410.14844/) (2024)
- [From Gaming to Research: GTA V for Synthetic Data Generation for Robotics and Navigations](https://mlsanity.com/paper/2502.12303/) (2025)
- [MonoCT: Overcoming Monocular 3D Detection Domain Shift with Consistent Teacher Models](https://mlsanity.com/paper/2503.13743/) (2025)
- [Paved2Paradise: Cost-Effective and Scalable LiDAR Simulation by Factoring the Real World](https://mlsanity.com/paper/2312.01117/) (2023)
- [Predicting Depth from Semantic Segmentation using Game Engine Dataset](https://mlsanity.com/paper/2106.15257/) (2021)
- [Randomized 3D Scene Generation for Generalizable Self-Supervised Pre-Training](https://mlsanity.com/paper/2306.04237/) (2023)
- [A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving Systems](https://mlsanity.com/paper/2312.15817/) (2023)
- [A workflow for generating synthetic LiDAR datasets in simulation environments](https://mlsanity.com/paper/2506.17378/) (2025)
- [PairedGTA: Generating Driving Datasets for Controlled Photometric Shift Analysis](https://mlsanity.com/paper/2606.01192/) (2026)
- [The Domain Is a Residue: Adapting Self-Supervised Features, Not Generators](https://mlsanity.com/paper/2609.37330/) (2026)

## Abstract (arXiv)

We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large detailed world with realistic graphics, which provides a diverse data collection environment. Existing works creating synthetic LiDAR data for autonomous driving with GTA V have not released their datasets, rely on an in-game raycasting function which represents people as cylinders, and can fail to capture vehicles past 30 metres. Our work creates a precise LiDAR simulator within GTA V which collides with detailed models for all entities no matter the type or position. The PreSIL dataset consists of over 50,000 frames and includes high-definition images with full resolution depth information, semantic segmentation (images), point-wise segmentation (point clouds), and detailed annotations for all vehicles and people. Collecting additional data with our framework is entirely automatic and requires no human annotation of any kind. We demonstrate the effectiveness of our dataset by showing an improvement of up to 5% average precision on the KITTI 3D Object Detection benchmark challenge when state-of-the-art 3D object detection networks are pre-trained with our data. The data and code are available at https://tinyurl.com/y3tb9sxy